[FIXED] Numpy: all the input arrays must have same number of dimensions

Issue

To easily calculate the dot product, I am writing a function to append a column with value 1 (concatenate at axis = 1), this is what I did:

def function(a):
    a = np.concatenate((a, np.ones(a.shape[0]), 1), axis=1)
    return a

train = function(train)
test = function(test)

When I run, I get this error:

ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 3 dimension(s) and the array at index 1 has 1 dimension(s)

I understand what it means, but I don't know how to fix this, can someone help me? Thanks for your help!

Solution

You can use a temporary variable to perform the task using Python list operations like append():

import numpy as np

def function(a):
    b = a.tolist()
    for i in range(len(b)):
        b[i].append(1)
    return np.array(b)

train = function(np.array([[0]*4096]*2400))
print(train.shape, train)

Output:

(2400, 4097) 
[[0 0 0 ... 0 0 1]
 [0 0 0 ... 0 0 1]
 [0 0 0 ... 0 0 1]
 ...
 [0 0 0 ... 0 0 1]
 [0 0 0 ... 0 0 1]
 [0 0 0 ... 0 0 1]]

Answered By - Cardstdani